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       "               0   32804   18312  15186  52785  20644  25927  10854  39616  \\\n",
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       "...          ...     ...     ...    ...    ...    ...    ...    ...    ...   \n",
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       "\n",
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       "...        ...    ...    ...    ...  \n",
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      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "f = open('./result4.csv', 'r')\n",
    "df = pandas.read_csv(f)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                               customer_id  \\\n",
       "0                                              customer_id   \n",
       "1        00000dbacae5abe5e23885899a1fa44253a17956c6d1c3...   \n",
       "2        0000423b00ade91418cceaf3b26c6af3dd342b51fd051e...   \n",
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       "4        00005ca1c9ed5f5146b52ac8639a40ca9d57aeff4d1bd2...   \n",
       "...                                                    ...   \n",
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       "1371965  ffff1c80bd2dede87deda612527c3df06d2fb270b85602...   \n",
       "\n",
       "                                                prediction  \n",
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       "...                                                    ...  \n",
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       "\n",
       "[1371966 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = open('./result3.csv','r')\n",
    "df = pandas.read_csv(f)\n",
    "df"
   ]
  }
 ],
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